Lead Data Engineer

There's a particular kind of frustration that comes from watching an entire industry run on spreadsheets, phone calls and email chains long after the technology to fix it exists. That's the gap this company is closing. They've built an AI assistant that takes over the operational grind for property managers, letting agents and build-to-rent teams: the compliance chasing, the maintenance coordination, the scheduling, the endless admin that eats up most of a property manager's week. Pilot customers are already live across thousands of units, and the feedback has been strong enough that the business is now gearing up for its next funding round.

The team sits at 20-30 people globally, with engineering at around 13 and mostly remote outside the core London group. It's a flat set-up: leads are simply the engineers who've earned it, and everyone reports into the Head of Engineering. Data hasn't had a dedicated owner yet, which is exactly why this role exists. You'd be the first senior data hire, building the architecture, tooling and standards from a blank page, and laying the groundwork for the data team that comes after you.

Day to day you'll be architecting and scaling the systems behind the AI product: real-time pipelines, analytics infrastructure, vector databases and the data workflows that feed the machine learning side of the platform. You'll work closely with the AI and backend engineers to make sure the platform can handle serious volumes of operational data reliably, and you'll have a genuine say in the technical architecture decisions that shape where this goes next.

• 7+ years in data engineering or backend engineering
• Strong track record designing and building data pipelines and distributed data systems
• Relational databases (PostgreSQL preferred, MySQL or similar acceptable)
• NoSQL databases
• Vector databases used in modern AI systems
• Strong Python

What You'll Work With

• Apache Spark
• Apache Airflow
• Kafka
• Elasticsearch / OpenSearch
• MongoDB
• Vector databases such as Qdrant, Milvus or pgvector
• Pandas and Polars

Nice to Haves

• JavaScript / Node.js alongside Python
• Experience on AI or machine learning platforms
• Stream processing and event-driven architectures
• Cloud infrastructure (GCP, AWS or Azure)
• Time in a high-growth startup or early-stage company